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首页> 外文期刊>The North American journal of economics and finance >Forecasting aggregate equity return volatility using crude oil price volatility: The role of nonlinearities and asymmetries
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Forecasting aggregate equity return volatility using crude oil price volatility: The role of nonlinearities and asymmetries

机译：使用原油价格波动率预测总股本收益率波动率：非线性和不对称性的作用

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摘要

To assess the potential nonlinear predictive impact of crude oil price volatility on aggregate equity return volatility, we consider autoregressions of monthly aggregate equity return realized volatility augmented with nonlinear transformations of crude oil price realized volatility and evaluate if they improve point forecasts. Out-of-sample results based on data from 1885m1 through 1895m12 and from 1983m1 through 2017m12 illustrate that our conclusions depend heavily on the notion of forecast improvement. At the population level, the null hypothesis of no out-of-sample predictability from crude oil price realized volatility to aggregate equity return realized volatility is rejected for the linear as well as certain nonlinear specifications. On the other hand, the null hypothesis of finite-sample equal predictive ability is rejected less frequently. Among the range of models, the autoregression augmented with the one-year net crude oil price realized volatility increase is the top performer, producing statistically significant more accurate point forecasts than the benchmark.
机译：为了评估原油价格波动对总股本收益率波动率的潜在非线性预测影响，我们考虑了每月总股票收益率已实现波动率的自回归与原油价格已实现收益率波动率的非线性转换，并评估它们是否会改善点预测。基于1885m1到1895m12以及1983m1到2017m12的数据的样本外结果表明，我们的结论在很大程度上取决于预测改进的概念。在总体水平上，线性和某些非线性指标都拒绝了从原油价格实现的波动率到总股本收益的实现的波动率没有样本外可预测性的零假设。另一方面，有限样本相等预测能力的零假设被拒绝的频率较低。在一系列模型中，自回归与一年净原油价格实现的波动性增加后的自回归是表现最佳的模型，与基准相比，该模型在统计上的准确性更高。

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